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YOLOv5_CSL_F: YOLOv5's Loss Improvement and Attention Mechanism Application for Remote Sensing Image Object Detection 3

YOLOv5_CSL_F: YOLOv5's Loss Improvement and Attention Mechan...

引用

3rd international conference on Wireless Communications and Smart Grid, ICWCSG 2021

作者： Wang, Junhua Xiao, Tao Gu, Qinyi Chen, Qian National University of Defense Technology Laboratory of Parallel and Distributed Processing Changsha China No.61646 Troop Department of Information Beijing China

ISBN: (纸本)9781665425988

With the continuous improvement of the resolution of satellite remote sensing images and aerial remote sensing images, more and more useful data and information are obtained from remote sensing images. At the same time, compared with ordinary images, remote sensing images have the characteristics of variable directions, unbalanced categories, complex backgrounds, and difficult detection of small objects. All of these make remote sensing image object detection very challenging. In this paper, based on the deep learning framework and the YOLOv5 object detection algorithm, according to the characteristics of remote sensing images, adopting Circular Smooth Label (CSL) [1] to calculate the loss of the rotating object detection bounding box and introducing the FcaNet [2] attention mechanism to design new feature fusion modules, we propose the remote sensing image object detection algorithm YOLOv5_CSL_F. We tested the algorithm model on the DOTA dataset. Compared with the detection performance of the original YOLOv5 algorithm, our algorithm improves the detection accuracy by 0.6%. © 2021 IEEE.

关键词： Object detection

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Deciding Backup Location Methods for distributed Stream processing System 3

Deciding Backup Location Methods for Distributed Stream Proc...

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3rd international conference on Information and Computer Technologies (ICICT)

作者： Iijima, Naoki Amemiya, Koichiro Ogawa, Jun Miyoshi, Hidenobu FUJITSU LABS LTD ICT Syst Labs Kawasaki Kanagawa Japan

ISBN: (纸本)9781728172835

A large amount of stream data are generated from some devices such as sensors and cameras. These stream data should be timely processed for real-time applications to satisfy the data latency requirements. To process a large amount of data in a short time, utilizing stream processing on edge/fog computing is a promising technology. In the stream processing system, a snapshot of processes and replications of the stream data are stored on another server, and when server fault or load spike of server occurs, the process is continued by using the stored snapshots and replicated data. Therefore, with edge computing environment, which has low bandwidth resource, process recovery takes a long time due to the transferring of restored data. In this paper, we propose a stream processing system architecture to decide servers to store snapshots and replication data and redeploy processes by considering the load of each server and the network bandwidth. We also propose a semi-optimal algorithm that reduces the computational cost by appropriately sorting servers and tasks according to the network bandwidth and server load. The algorithm can find a solution over 1000 times faster than the Coin or Branch and Cut (CBC) solver.

关键词： distributed system stream processing fault recovery

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Ladder: A Blockchain Model of Low-Overhead Storage 3rd

Ladder: A Blockchain Model of Low-Overhead Storage

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3rd international conference on Blockchain and Trustworthy Systems, Blocksys 2021

作者： Chen, Peng Zhang, Xunhui Liu, Hui Xiang, Liangliang Shi, Peichang National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789811679926

Emerging blockchain accounting mechanism allow mutually distributed parties to transport trusted information and ensure the correctness of data. Every blockchain node stores the complete block locally. Although this measure improves security, it causes huge storage overhead. We present Ladder, a multi-level low-overhead storage model for blockchain, which can greatly reduce the storage overhead of nodes while ensuring that the blockchain cannot be tampered easily and the block information is complete. Ladder distinguishes the retrieved frequency and importance of different blocks by value-density, dividing them into Hot-Blocks, Warm-Blocks and Cold-Blocks adopting different storage strategies respectively. Through experimental verification of time and space cost of different storage strategies, Ladder can greatly reduce the local storage overhead of nodes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Colony: parallel Functions as a Service on the Cloud-Edge Continuum 27th

Colony: Parallel Functions as a Service on the Cloud-Edge Co...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Lordan, Francesc Lezzi, Daniele Badia, Rosa M. Barcelona Supercomp Ctr BSC Dept Comp Sci Barcelona Spain

ISBN: (纸本)9783030856656;9783030856649

Although smart devices markets are increasing their sales figures, their computing capabilities are not sufficient to provide good-enough-quality services. This paper proposes a solution to organize the devices within the Cloud-Edge Continuum in such a way that each one, as an autonomous individual -Agent-, processes events/data on its embedded compute resources while offering its computing capacity to the rest of the infrastructure in a Function-as-a-Service manner. Unlike other FaaS solutions, the described approach proposes to transparently convert the logic of such functions into task-based workflows backing on task-based programming models;thus, agents hosting the execution of the method generate the corresponding workflow and offloading part of the workload onto other agents to improve the overall service performance. On our prototype, the function-to-workflow transformation is performed by COMPSs;thus, developers can efficiently code applications of any of the three envisaged computing scenarios - sense-process-actuate, streaming and batch processing - throughout the whole Cloud-Edge Continuum without struggling with different frameworks specifically designed for each of them.

关键词： Edge Fog Cloud Compute Continuum distributed systems Programming model Runtime system Serverless Function-as-a-Service Stream-processing Task-based workflow

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Accelerating Graph applications Using Phased Transactional Memory 27th

Accelerating Graph Applications Using Phased Transactional M...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Morales, Catalina Munoz Murari, Rafael de Carvalho, Joao P. L. Honorio, Bruno Chinelato Baldassin, Alexandro Araujo, Guido Univ Estadual Campinas Inst Comp Campinas Brazil Univ Estadual Paulista UNESP Rio Claro Brazil

ISBN: (纸本)9783030856656;9783030856649

Due to their fine-grained operations and low conflict rates, graph processing algorithms expose a large amount of parallelism that has been extensively exploited by various parallelization frameworks. Transactional Memory (TM) is a programming model that uses an optimistic concurrency control mechanism to improve the performance of irregular applications, making it a perfect candidate to extract parallelism from graph-based programs. Although fast Hardware TM (HTM) instructions are now available in the ISA extensions of some major processor architectures (e.g., Intel and ARM), balancing the usage of Software TM (STM) and HTM to compensate for capacity and conflict aborts is still a challenging task. This paper presents a Phased TM implementation for graph applications, called Graph-Oriented Transactional Memory (GoTM). It uses a three-state (HTM, STM, GLOCK) concurrency control automaton that leverages both HTM and STM implementations to speed-up graph applications. Experimental results using seven well-known graph programs and real-life workloads show that GoTM can outperform other Phased TM systems and lock-based concurrency mechanisms such as the one present in Galois, a state-of-the-art framework for graph computations.

关键词： Hardware transactional memory Software transactional memory Graph processing Large-scale graphs

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Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection

Benchmarking Quantum Convolutional Neural Networks for Signa...

引用

Euromicro conference on parallel, distributed and Network-Based processing

作者： Farida Farsian Nicoló Parmiggiani Alessandro Rizzo Gabriele Panebianco Andrea Bulgarelli Francesco Schilliró Carlo Burigana Vincenzo Cardone Luca Cappelli Massimo Meneghetti Giuseppe Murante Giuseppe Sarracino Roberto Scaramella Vincenzo Testa Tiziana Trombetti OACT INAF Via S. Sofia 78 Catania Italy OAS INAF Via Pietro Gobetti 93/3 Bologna Italy IRA INAF Via Pietro Gobetti 101 Bologna Italy OAR INAF Via Frascati 33 Monteporzio Catone Italy OATs INAF Via Tiepolo 11 Trieste Italy OACN INAF Via Moiariello 16 Napoli Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional Neural Networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis.

关键词： Observatories Accuracy Quantum advantage Gamma-ray bursts Qubit Benchmark testing Telescopes Encoding Convolutional neural networks Signal detection

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PrioRAT: Criticality-Driven Prioritization Inside the On-Chip Memory Hierarchy 27th

PrioRAT: Criticality-Driven Prioritization Inside the On-Chi...

引用

27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Dimic, Vladimir Moreto, Miquel Casas, Marc Valero, Mateo Barcelona Supercomp Ctr BSC Barcelona Spain Univ Politecn Catalunya UPC Barcelona Spain

ISBN: (纸本)9783030856656;9783030856649

The ever-increasing gap between the processor and main memory speeds requires careful utilization of the limited memory link. This is additionally emphasized for the case of memory-bound applications. Prioritization of memory requests in the memory controller is one of the approaches to improve performance of such codes. However, current designs do not consider high-level information about parallel applications. In this paper, we propose a holistic approach to this problem, where the runtime system-level knowledge is made available in hardware. Processor exploits this information to better prioritize memory requests, while introducing negligible hardware cost. Our design is based on the notion of critical path in the execution of a parallel code. The critical tasks are accelerated by prioritizing their memory requests within the on-chip memory hierarchy. As a result, we reduce the critical path and improve the overall performance up to 1.19 x compared to the baseline systems.

关键词： parallel processing systems

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Performance Meets Programmabilty: Enabling Native Python MPI Tasks In PyCOMPSs 28

Performance Meets Programmabilty: Enabling Native Python MPI...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Elshazly, Hatem Lordan, Fratacesc Ejarque, Jorge Badia, Rosa M. Barcelona Supercomp Ctr BSC Dept Comp Sci Barcelona Spain

ISBN: (纸本)9781728165820

The increasing complexity of modern and future computing systems makes it challenging to develop applications that aim for maximum performance. Hybrid parallel programming models offer new ways to exploit the capabilities of the underlying infrastructure. However, the performance gain is sometimes accompanied by increased programming complexity. We introduce an extension to PyCOMPSs, a high-level task-based parallel programming model for Python applications, to support tasks that use MPI natively as part of the task model. Without compromising application's programmability, using Native MPI tasks in PyCOMPSs offers up to 3x improvement in total performance for compute intensive applications and up to 1.9x improvement in total performance for 110 intensive applications over sequential implementation of the tasks.

关键词： Hybrid Programming Models distributed Computing MPI High Performance Computing Task-based parallel Programming Models Performance Productivity

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Performance study of HPC applications on an Arm-based cluster using a generic efficiency model 28

Performance study of HPC applications on an Arm-based cluste...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Banchelli, Fabio Peiro, Kilian Querol, Andrea Ramirez-Gargallo, Guillem Ramirez-Miranda, Guillem Vinyals, Joan Vizcaino, Pablo Garcia-Gasulla, Marta Mantovani, Filippo Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781728165820

HPC systems and parallel applications are increasing their complexity. Therefore the possibility of easily study and project at large scale the performance of scientific applications is of paramount importance. In this paper we describe a performance analysis method and we apply it to four complex HPC applications. We perform our study on a pre-production HPC system powered by the latest Arm-based CPUs for HPC, the Marvell ThunderX2. For each application we spot inefficiencies and factors that limit their scalability. The results show that in several cases the bottlenecks do not come from the hardware but from the way applications are programmed or the way the system software is configured.

关键词： Performance analysis High Performance Computing parallel applications Arm ThunderX2

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Squeeze-and-Excitation network-Based Radar Object Detection With Weighted Location Fusion 21

Squeeze-and-Excitation network-Based Radar Object Detection ...

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11th international conference on Multimedia Retrieval (ICMR)

作者： Sun, Pengliang Niu, Xuetong Sun, Pengfei Xu, Kele Chinese Univ Hong Kong Hong Kong Peoples R China Kings Coll London London England Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781450384636

Radar object detection refers to identify objects from radar data, and the topic has received increasing interest during the last years, due to the appealing property of radar imaging and evident applications. However, the detection performance heavily relied on semantic information extraction, which is a great challenge in practical settings. Moreover, although remarkable progress has been made, most previous attempts are restrained from the essentially limited property of the employed single modality. Inspired by the recent success of cross-modality deep learning, we propose a novel cross-modality deep learning framework for the radar object detection task using the Squeeze-and-Excitation network, aiming to provide more powerful feature representation. Moreover, a novel noisy detection approach is also explored in our study, to increase the model's ability to handle with noise. Finally, a novel weighted location fusion strategy is introduced in our framework, to improve the detection performance further. To empirically investigate the effectiveness of the proposed framework, we conduct extensive experiments on the 2021 ICMR ROD challenge. The obtained results suggest that our framework outperforms related approaches. Our method ranks as the 3rd place on the final leaderboard, with an average precision (AP) percentage of 76.1. Models and codes are available at https://***/sunpengliang/modelConfusion.

关键词： Cross-modal machine learning Ensemble learning Radar object detection

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